面向用户用电行为检测的协同优化联邦学习框架、数据二维分解策略和隐私优化博弈模型OA

Data barriers exist in power metering systems,hindering cross-entity data sharing and integration,which leads to low accuracy in data-driven identification of abnormal electricity consumption behaviors.While federated learning can alleviate data silos,traditional methods struggle to meet the diverse needs of different entities regarding anomaly features.Additionally,issues such as insufficient privacy protection and lack of incentive mechanisms persist.To address these limitations,this study proposes a collaborative optimization federated learning framework that balances privacy and utility.The framework incorporates several key innovations.First,it employs wavelet decomposition to segregate user electricity data into approximation and detail coefficients,separating common and individual characteristics as well as low-sensitivity and high-sensitivity data components.Then,an optimal differential privacy strategy is derived through a master-slave game model,incentivizing power entities to share high-value raw data while balancing privacy protection and data utility.Finally,based on the optimal personalized privacy budget obtained from the game mode,a hierarchical differential protection is applied to highly sensitive personalized models.This approach integrates a novel federated aggregation method,combining average weight parameters from power entities and magnitude weight parameters from metering centers.It enhances the local adaptability of power entity models and the global universality of metering center models while ensuring robust data privacy and security.Experimental results on an abnormal electricity usage detection dataset demonstrate the effectiveness of the proposed framework in improving detection accuracy while maintaining data privacy and utility.